Field of the Invention
The present invention relates to a device and to a process for motion-compensated recursive filtering, as well as to a corresponding coding system.
It pertains to noise reduction techniques applied to digital video signals. These techniques are applied generally to digital video images taking the form of a matrix of samples; each sample is composed of a luminance signal and, for a colour signal, of a chrominance signal.
The acquisition of video image sequences is still today largely carried out in analogue form so that the images, once acquired and possibly transmitted and stored in analogue formats, exhibit an appreciable share of noise in their content. Once digitized, these images are also often subjected to storage/editing operations which, in turn, introduce noise, this time of a digital nature. Finally, an image sequence generally undergoes a succession of transformations, the result of which is spatio-temporal noise of a highly random nature.
To obtain high-performance operation, the noise reduction methods calling upon recursive filtering consider the very strong temporal correlation of the images of a video sequence. Consequently, the concepts of motion and of displacement are important for achieving effective noise reduction.
“Displacement” is understood to mean the change of position of an object in a scene, this change of position being localized and specific to this object. “Motion” is understood to mean all of the displacements of objects in a video sequence.
Motion is conventionally detected either by simple image-to-image differencing, or by using a motion estimator. In the first case, pixel-to-pixel image differences or FDs (Frame Differences) at distinct instants are employed.
In the second case (use of a motion estimator), the displacements are taken into account by performing image differencing at distinct instants. These displacements are represented by motion vector fields applied to pixels (pixelwise motion estimation) or to blocks (blockwise motion estimation). Motion-compensated image differences are thus obtained, called DFDs (Displacement Frame Differences), pixelwise or blockwise. The second method gives much better results than the first, at the cost of greater complexity.
According to known techniques of noise reduction by motion-compensated recursive filtering, a weighting of a current image signal u and of a predictive signal v, obtained by means of an earlier motion-compensated and then filtered input signal, is performed. The weighted signal thus obtained is produced at output as the filtered signal and is recorded for later instants. The weighting coefficient of the current input signal u and the weighting coefficient of the predictive signal v are based on the calculation of a recursivity coefficient α. In high-performance systems, this coefficient is calculated on the basis of a noise estimate and of the current prediction error, equal to the difference between the current signal u and the predictive signal v.
Generally, the determination of the recursivity coefficient α is based on noise level estimation, often obtained from information about DFDs emanating from a motion estimator. Specifically, the DFDs are fairly representative of the amount of noise contained in a video sequence. The DFDs even give an exact expression the therefor if the motion compensation is ideal (that is to say if the compensation is perfect, whatever the nature of the motion and the deformations).
These techniques afford significant advantages: the reduction in noise applies over the entire image, the estimation of the noise level is performed over the whole image and the risks of confusion between noise and useful signal are minimized. However, the estimation of the actual noise level is biased because the motion estimator is not ideal. Specifically, the accuracy of the estimator is limited by a certain number of factors arising on the one hand from implementational constraints (accuracy of coding of the motion vectors, maximum excursion of these vectors, etc.) and on the other hand from constraints intrinsic to the motion estimators (for example difficulties in reliably estimating rotational motions or those originating from a homothety). Moreover, the use of a motion estimation algorithm based on the concept of pixel blocks (commonly called “Block Matching”) accentuates some of these defects. This lack of accuracy, also dependent on the source and on its quality (noisy or otherwise), gives rise to an overestimate (on account of an increase in the DFDs) of the actual noise level. Consequently, the filtering is too severe causing a blurring effect. Finally, in the presence of noise and in static zones, the sensitivity of the motion estimator to noise may cause a lack of homogeneity of the vector field giving rise to a real distortion of filtering of these zones. This degradation, which is manifested as a temporal flickering and swarming effect, is detrimental and hardly acceptable.